NDLI: A verification methodology for prognostic algorithms

Content Provider	IEEE Xplore Digital Library
Author	Bin Zhang Liang Tang DeCastro, J. Goebel, K.
Copyright Year	2010
Description	Author affiliation: Impact Technologies, LLC, 200 Canal View Blvd., Rochester NY 14623 (Bin Zhang; Liang Tang; DeCastro, J.) \|\| NASA AMES center, Mountain View, CA (Goebel, K.)
Abstract	Prognosis is a fundamental enabling technique for condition-based maintenance (CBM) systems and prognostics and health management (PHM) systems and therefore, plays a critical role in the successful deployment of these systems. The purpose of prognosis is to predict the remaining useful life of a system/subsystem or a component when a fault is detected. Although different prognostic algorithms have been developed and tentatively applied to various mechanical and electrical systems in the past decade, the verification and validation (V&V) remains a challenging open problem. The difficulties lie in the facts that first, there is usually no statistically sufficient data to do V&V and second, there is no rigorous and general V&V framework available. In this paper, a verification methodology based on exact filtering and Monte Carlo method is proposed to verify a user defined particle-filtering based prognostic algorithm. The methodology is a general one that can be extended to verification of other prognostic algorithms straightforward. When statistically sufficient data are available, validation can be implemented under the similar framework. The verification methodology is demonstrated on the prognosis of a seeded-fault planetary helicopter gearbox carrier.
Starting Page	1
Ending Page	8
File Size	915839
Page Count	8
File Format	PDF
ISBN	9781424479603
ISSN	10887725
e-ISBN	9781424479610
DOI	10.1109/AUTEST.2010.5613615
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo methods Filtering Uncertainty Mathematical model Data models Prediction algorithms Computational modeling Particle Filtering Verification and Validation Prognostics Exact Filtering Monte Carlo Method
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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